Key “rules“: Key “constructor-super“: structuredClone is not defined

vue3的项目,配置好eslint后,使用启动lint检查,报错

由于项目所使用的 Node.js 版本过低,而 ESLint 或者其相关插件在执行过程中使用的方法structuredClone是在 Node.js 17 及以上版本才支持的内置方法,

因此只需要进行升级nodejs就好了

推荐使用nvm来管理多个node版本

升级成功后,在诚心启动就OK啦

### Gated Recurrent Convolution UNet Implementation and Explanation #### Overview of the Architecture The architecture combines elements from both U-Net models, which are widely used in image segmentation tasks, with gated recurrent convolutions that introduce a temporal dependency into convolutional operations. This combination allows for more effective feature extraction over sequential data or multi-channel images[^1]. #### Key Components In this model, each layer consists of two main parts: an encoder path and a decoder path connected by skip connections. The unique aspect lies within how these layers process information through gates. A gate mechanism controls what part of previous states should be passed forward while allowing new inputs to update existing representations dynamically during training phases[^2]. For implementing such networks programmatically using PyTorch framework: ```python import torch.nn as nn class GRConvBlock(nn.Module): def __init__(self, in_channels, out_channels): super(GRConvBlock, self).__init__() # Define standard convolution operation followed by batch normalization. self.conv = nn.Sequential( nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels)) # Initialize gating function components. self.reset_gate = nn.Conv2d(in_channels + out_channels, out_channels, kernel_size=3, padding=1) self.update_gate = nn.Conv2d(in_channels + out_channels, out_channels, kernel_size=3, padding=1) def forward(self, x_prev, h_prev=None): if h_prev is None: shape = (x_prev.shape[0], self.out_channels) + x_prev.shape[-2:] h_prev = torch.zeros(shape).to(x_prev.device) combined_input = torch.cat([h_prev, x_prev], dim=1) r_t = torch.sigmoid(self.reset_gate(combined_input)) z_t = torch.sigmoid(self.update_gate(combined_input)) candidate_state = torch.tanh(self.conv(r_t * h_prev)) hidden_state = (1-z_t)*candidate_state+z_t*h_prev output = hidden_state.clone() return output, hidden_state ``` This code snippet defines one block containing gated recurrent convolution logic where `forward` method takes current input along with optional past state (`None` at first call), applies reset/update rules on concatenated tensor formed between them before passing it further down network pipeline via defined sequence inside constructor initialization phase[^3].
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